By Tao Mei, Ruofei (Bruce) Zhang, Xian-Sheng Hua
Microsoft Research Asia, China
Yahoo! Labs, USA
tmei@microsoft.com
rzhang@yahoo-inc.com
xshua@microsoft.com
Advertising provides financial support for a large portion of today’s Internet ecosystem. Compared to traditional means of advertising, such as a banner outside a store or textual advertisements in newspapers, multimedia advertising has some unique advantages: it is more attractive and more salient than plain text, it is able to instantly grab users’ attention and it carries more information that can also be comprehended more quickly than when reading a text advertisement. Rapid convergence of multimedia, Internet and mobile devices has opened new opportunities for manufacturers and advertisers to more effectively and efficiently reach potential customers. While largely limiting itself to radio and TV channels currently, multimedia advertising is about to break through on the web using various concepts of internet multimedia advertising.
The explosive growth of multimedia data on the web also creates huge opportunities for further monetizing them with multimedia advertisements. Multimedia content becomes a natural information carrier for advertising in a way similar to radio wave to carry bits in digital communications. More and more business models are rolled out to freely distribute multimedia contents and recoup the revenue from the multimedia advertisements it carries. With the increasing importance of online multimedia advertising, researchers from multimedia community have made significant progresses along this direction. This tutorial aims at: 1) reviewing and summarizing recent high-quality research works on internet multimedia advertising, including basic technologies and applicable systems, and 2) presenting insight into the challenges and future directions in this emerging and promising area.
This tutorial is appropriate to ACM Multimedia, including both graduate students and senior researchers working in the field of multimedia and/or online advertising, as well as industry practitioners who are working in the field of search engine development, video/image content providers, developers of video/image sharing portals and IPTV providers. Instead of in depth coverage of contemporary papers, in this three hour tutorial, we plan to introduce the important general concepts and themes of this timely topic which are interesting to the MM audience. Moreover, we will also show extensive demos on contextual multimedia advertising.
Tentative outline of the tutorial:
- Introduction of traditional text advertising techniques
- Understand audience for user-targeted advertising
- Image advertising
- Video advertising
- Mobile advertising
- Challenges and future directions
BIOS OF PRESENTERS
Tao Mei is a Researcher from Microsoft Research Asia, Beijing, China. His current research interests include multimedia content analysis, computer vision, and multimedia applications such as search, advertising, social networking, and mobile computing. He is the editor of one book, the author of seven book chapters and over 100 journal and conference papers, in these areas, and holds more than 30 filed or pending applications. He serves as an Associate Editor for Neurocomputing and Journal of Multimedia, a Guest Editor for IEEE Multimedia, ACM/Springer Multimedia Systems, and Journal of Visual Communication and Image Representation. He was the principle designer of the automatic video search system that achieved the best performance in the worldwide TRECVID evaluation in 2007. He received the Best Paper and Best Demonstration Awards in ACM Multimedia 2007, the Best Poster Paper Award in IEEE MMSP 2008, and the Best Paper Award in ACM Multimedia 2009. He was awarded Microsoft Gold Star in 2010. Tao received the B.E. and the Ph.D. degrees from the University of Science and Technology of China, Hefei, in 2001 and 2006, respectively.
Ruofei (Bruce) Zhang is a Senior Scientist in the Advertising Sciences division at Yahoo! Labs, Silicon Valley. He currently manages information retrieval modeling, response prediction and optimization group that applies statistical machine learning and time series analysis techniques to solve problems in contextual and display advertising. Bruce joined Yahoo! in June 2005. Prior to working at Yahoo! Labs, he had worked on sponsored search query rewriting modeling in Search and Advertising Sciences and led search relevance R&D in Yahoo! Video Search. Bruce’s research interests are in machine learning, large scale data analysis and mining, optimization, time series analysis, image/video processing and analysis, and multimedia information retrieval. He has co-authored a monograph book on multimedia data mining and published over two dozen peer-reviewed papers on leading international journals and conferences and several invited papers and book chapters; he is inventor or co-inventor of more than 20 issued and pending patents on online advertising, search relevance, ranking function learning, and multimedia content analysis. Bruce has been serving on the grant review panels for US NSF and program committee of major conferences in the fields. Bruce received a Ph. D. in computer science with Distinguished Dissertation Award from State University of New York at Binghamton; a M.E. and B.E. from Tsinghua University and Xi’an Jiaotong University, respectively.
Xian-Sheng Hua is now a Principal Research and Development Lead for Bing Multimedia Search with Microsoft. He is responsible for driving a team to design and deliver thought-leading media understanding and indexing features. Before joining Bing in 2011, Dr. Hua was a Lead Researcher with Microsoft Research Asia. During that time, his research interests are in the areas of multimedia search, advertising, understanding, and mining, as well as pattern recognition and machine learning. He has authored or co-authored more than 180 publications in these areas and has more than 60 filed patents or pending applications. Xian-Sheng Hua received the B.S. and Ph.D. degrees from Peking University, Beijing, China, in 1996 and 2001, respectively, both in applied mathematics. He serves as an Associate Editor of IEEE Transactions on Multimedia, Associate Editor of ACM Transactions on Intelligent Systems and Technology, Editorial Board Member of Advances in Multimedia and Multimedia Tools and Applications, and editor of Scholarpedia (Multimedia Category). Dr. Hua won the Best Paper Award and Best Demonstration Award in ACM Multimedia 2007, Best Poster Award in 2008 IEEE International Workshop on Multimedia Signal Processing, Best Student Paper Award in ACM Conference on Information and Knowledge Management 2009, and Best Paper Award in International Conference on MultiMedia Modeling 2010. He also won 2008 MIT Technology Review TR35 Young Innovator Award for his outstanding contributions to video search.